{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:10:07.753054100Z",
     "start_time": "2024-01-29T11:10:06.476311900Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1100 entries, 0 to 1099\n",
      "Data columns (total 31 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Attrition                 1100 non-null   int64 \n",
      " 1   Age                       1100 non-null   int64 \n",
      " 2   BusinessTravel            1100 non-null   object\n",
      " 3   Department                1100 non-null   object\n",
      " 4   DistanceFromHome          1100 non-null   int64 \n",
      " 5   Education                 1100 non-null   int64 \n",
      " 6   EducationField            1100 non-null   object\n",
      " 7   EmployeeNumber            1100 non-null   int64 \n",
      " 8   EnvironmentSatisfaction   1100 non-null   int64 \n",
      " 9   Gender                    1100 non-null   object\n",
      " 10  JobInvolvement            1100 non-null   int64 \n",
      " 11  JobLevel                  1100 non-null   int64 \n",
      " 12  JobRole                   1100 non-null   object\n",
      " 13  JobSatisfaction           1100 non-null   int64 \n",
      " 14  MaritalStatus             1100 non-null   object\n",
      " 15  MonthlyIncome             1100 non-null   int64 \n",
      " 16  NumCompaniesWorked        1100 non-null   int64 \n",
      " 17  Over18                    1100 non-null   object\n",
      " 18  OverTime                  1100 non-null   object\n",
      " 19  PercentSalaryHike         1100 non-null   int64 \n",
      " 20  PerformanceRating         1100 non-null   int64 \n",
      " 21  RelationshipSatisfaction  1100 non-null   int64 \n",
      " 22  StandardHours             1100 non-null   int64 \n",
      " 23  StockOptionLevel          1100 non-null   int64 \n",
      " 24  TotalWorkingYears         1100 non-null   int64 \n",
      " 25  TrainingTimesLastYear     1100 non-null   int64 \n",
      " 26  WorkLifeBalance           1100 non-null   int64 \n",
      " 27  YearsAtCompany            1100 non-null   int64 \n",
      " 28  YearsInCurrentRole        1100 non-null   int64 \n",
      " 29  YearsSinceLastPromotion   1100 non-null   int64 \n",
      " 30  YearsWithCurrManager      1100 non-null   int64 \n",
      "dtypes: int64(23), object(8)\n",
      "memory usage: 266.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:08:09.742405800Z",
     "start_time": "2024-01-29T11:08:09.727671700Z"
    }
   },
   "id": "1d2eabe4480562c5",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "         Attrition          Age  DistanceFromHome    Education  \\\ncount  1100.000000  1100.000000       1100.000000  1100.000000   \nmean      0.161818    36.999091          9.427273     2.922727   \nstd       0.368451     9.037230          8.196694     1.022242   \nmin       0.000000    18.000000          1.000000     1.000000   \n25%       0.000000    30.000000          2.000000     2.000000   \n50%       0.000000    36.000000          7.000000     3.000000   \n75%       0.000000    43.000000         15.000000     4.000000   \nmax       1.000000    60.000000         29.000000     5.000000   \n\n       EmployeeNumber  EnvironmentSatisfaction  JobInvolvement     JobLevel  \\\ncount     1100.000000              1100.000000     1100.000000  1100.000000   \nmean      1028.157273                 2.725455        2.730909     2.054545   \nstd        598.915204                 1.098053        0.706366     1.107805   \nmin          1.000000                 1.000000        1.000000     1.000000   \n25%        504.250000                 2.000000        2.000000     1.000000   \n50%       1026.500000                 3.000000        3.000000     2.000000   \n75%       1556.500000                 4.000000        3.000000     3.000000   \nmax       2065.000000                 4.000000        4.000000     5.000000   \n\n       JobSatisfaction  MonthlyIncome  ...  RelationshipSatisfaction  \\\ncount      1100.000000    1100.000000  ...               1100.000000   \nmean          2.732727    6483.620909  ...                  2.696364   \nstd           1.109731    4715.293419  ...                  1.095356   \nmin           1.000000    1009.000000  ...                  1.000000   \n25%           2.000000    2924.500000  ...                  2.000000   \n50%           3.000000    4857.000000  ...                  3.000000   \n75%           4.000000    8354.500000  ...                  4.000000   \nmax           4.000000   19999.000000  ...                  4.000000   \n\n       StandardHours  StockOptionLevel  TotalWorkingYears  \\\ncount         1100.0       1100.000000        1100.000000   \nmean            80.0          0.788182          11.221818   \nstd              0.0          0.843347           7.825548   \nmin             80.0          0.000000           0.000000   \n25%             80.0          0.000000           6.000000   \n50%             80.0          1.000000          10.000000   \n75%             80.0          1.000000          15.000000   \nmax             80.0          3.000000          40.000000   \n\n       TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\ncount            1100.000000      1100.000000     1100.000000   \nmean                2.807273         2.746364        7.011818   \nstd                 1.291514         0.701121        6.223093   \nmin                 0.000000         1.000000        0.000000   \n25%                 2.000000         2.000000        3.000000   \n50%                 3.000000         3.000000        5.000000   \n75%                 3.000000         3.000000        9.000000   \nmax                 6.000000         4.000000       37.000000   \n\n       YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \ncount         1100.000000              1100.000000           1100.000000  \nmean             4.207273                 2.226364              4.123636  \nstd              3.618115                 3.313830              3.597996  \nmin              0.000000                 0.000000              0.000000  \n25%              2.000000                 0.000000              2.000000  \n50%              3.000000                 1.000000              3.000000  \n75%              7.000000                 3.000000              7.000000  \nmax             18.000000                15.000000             17.000000  \n\n[8 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Attrition</th>\n      <th>Age</th>\n      <th>DistanceFromHome</th>\n      <th>Education</th>\n      <th>EmployeeNumber</th>\n      <th>EnvironmentSatisfaction</th>\n      <th>JobInvolvement</th>\n      <th>JobLevel</th>\n      <th>JobSatisfaction</th>\n      <th>MonthlyIncome</th>\n      <th>...</th>\n      <th>RelationshipSatisfaction</th>\n      <th>StandardHours</th>\n      <th>StockOptionLevel</th>\n      <th>TotalWorkingYears</th>\n      <th>TrainingTimesLastYear</th>\n      <th>WorkLifeBalance</th>\n      <th>YearsAtCompany</th>\n      <th>YearsInCurrentRole</th>\n      <th>YearsSinceLastPromotion</th>\n      <th>YearsWithCurrManager</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>...</td>\n      <td>1100.000000</td>\n      <td>1100.0</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.161818</td>\n      <td>36.999091</td>\n      <td>9.427273</td>\n      <td>2.922727</td>\n      <td>1028.157273</td>\n      <td>2.725455</td>\n      <td>2.730909</td>\n      <td>2.054545</td>\n      <td>2.732727</td>\n      <td>6483.620909</td>\n      <td>...</td>\n      <td>2.696364</td>\n      <td>80.0</td>\n      <td>0.788182</td>\n      <td>11.221818</td>\n      <td>2.807273</td>\n      <td>2.746364</td>\n      <td>7.011818</td>\n      <td>4.207273</td>\n      <td>2.226364</td>\n      <td>4.123636</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.368451</td>\n      <td>9.037230</td>\n      <td>8.196694</td>\n      <td>1.022242</td>\n      <td>598.915204</td>\n      <td>1.098053</td>\n      <td>0.706366</td>\n      <td>1.107805</td>\n      <td>1.109731</td>\n      <td>4715.293419</td>\n      <td>...</td>\n      <td>1.095356</td>\n      <td>0.0</td>\n      <td>0.843347</td>\n      <td>7.825548</td>\n      <td>1.291514</td>\n      <td>0.701121</td>\n      <td>6.223093</td>\n      <td>3.618115</td>\n      <td>3.313830</td>\n      <td>3.597996</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>18.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1009.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>30.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>504.250000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2924.500000</td>\n      <td>...</td>\n      <td>2.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>36.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>1026.500000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>4857.000000</td>\n      <td>...</td>\n      <td>3.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>43.000000</td>\n      <td>15.000000</td>\n      <td>4.000000</td>\n      <td>1556.500000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>8354.500000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>9.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>60.000000</td>\n      <td>29.000000</td>\n      <td>5.000000</td>\n      <td>2065.000000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>4.000000</td>\n      <td>19999.000000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>3.000000</td>\n      <td>40.000000</td>\n      <td>6.000000</td>\n      <td>4.000000</td>\n      <td>37.000000</td>\n      <td>18.000000</td>\n      <td>15.000000</td>\n      <td>17.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:08:19.175749Z",
     "start_time": "2024-01-29T11:08:19.113276500Z"
    }
   },
   "id": "481768e8bfac3b0d",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 都是数值, 取值少的实际还是类别, 比如 Education  1,2,3,4,5 5个类别\n",
    "- 取值多的比如 Age年龄  连续数值 "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f35833b8aa11a19d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 二分类问题, 目标值是类别, 特征值是连续多值数值型, 两种方式 画图 KDE  单特征AUC"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e3b2aee11fcb32bb"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x176d5cbfad0>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.kdeplot(data = data[data['Attrition']==1], x='Age',label = 'Attr')\n",
    "sns.kdeplot(data = data[data['Attrition']==0], x='Age',label = 'not')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='best')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:50:01.197504600Z",
     "start_time": "2024-01-29T11:50:00.962741200Z"
    }
   },
   "id": "1555ddb0195596a0",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x176d5de08d0>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.kdeplot(data = data[data['Attrition']==1], x='MonthlyIncome',label = 'Attr')\n",
    "sns.kdeplot(data = data[data['Attrition']==0], x='MonthlyIncome',label = 'not')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='best')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:51:00.999456900Z",
     "start_time": "2024-01-29T11:51:00.740361Z"
    }
   },
   "id": "bd643f90406022bf",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.351735967242682"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(data['Attrition'],data['Age'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:52:03.693666100Z",
     "start_time": "2024-01-29T11:52:03.424322500Z"
    }
   },
   "id": "31060b311baee253",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.3448505934826586"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['MonthlyIncome'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:52:56.703261200Z",
     "start_time": "2024-01-29T11:52:56.696680200Z"
    }
   },
   "id": "e993d1a4756644b8",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.15514940651734138"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5-roc_auc_score(data['Attrition'],data['MonthlyIncome'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:53:11.119059400Z",
     "start_time": "2024-01-29T11:53:11.112838400Z"
    }
   },
   "id": "3309ce0bc0ad2273",
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 类别型"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6d64f4a4743fd843"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Education\n1    0.214286\n2    0.145631\n3    0.167053\n4    0.156146\n5    0.055556\nName: Attrition, dtype: float64"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Education')['Attrition'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:54:31.964555200Z",
     "start_time": "2024-01-29T11:54:31.951652600Z"
    }
   },
   "id": "357dcf4cde17968c",
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.47068841551098006"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:55:30.022366200Z",
     "start_time": "2024-01-29T11:55:29.999251400Z"
    }
   },
   "id": "141cb03fbebaf587",
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "JobInvolvement\n1    0.380952\n2    0.168498\n3    0.146747\n4    0.106796\nName: Attrition, dtype: float64"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('JobInvolvement')['Attrition'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:56:04.410411900Z",
     "start_time": "2024-01-29T11:56:04.393200900Z"
    }
   },
   "id": "a973d377849ac5ef",
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.42820322211118966"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['JobInvolvement'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:56:16.085841200Z",
     "start_time": "2024-01-29T11:56:16.071798700Z"
    }
   },
   "id": "307b7fd1de9e1079",
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 所有特征按照这个套路遍历一圈, 可以找出有区分度的特征, 0.5附近 0.48 0.52 直接删除了\n",
    "# 先跑一个baseline "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bb66eac6d8d7cee"
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 进一步提升效果\n",
    " - 类别的可以进行编码\n",
    "    - 用离职率来编码\n",
    "    - woe p_good/p_bad   P_没离职/P_离职  toad\n",
    " - 数值型 分箱/编码\n",
    "    - 卡方分箱 WOE变换\n",
    "    - 自己做分箱尝试 /决策树 \n",
    " - 调整了一个特征就可以跑一下模型和baseline做比较"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "749e1e3ebf5cc0e0"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.47068841551098006"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:00:57.921019200Z",
     "start_time": "2024-01-29T12:00:57.903601Z"
    }
   },
   "id": "2a585ca19124858c",
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.029311584489019937"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5-roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:01:19.042267800Z",
     "start_time": "2024-01-29T12:01:19.017528900Z"
    }
   },
   "id": "bd87d8786b1e3550",
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.5448737478368959"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_encoding_dict = data.groupby('Education')['Attrition'].mean().to_dict()\n",
    "roc_auc_score(data['Attrition'],data['Education'].replace(edu_encoding_dict))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:01:05.472588900Z",
     "start_time": "2024-01-29T12:01:05.454202600Z"
    }
   },
   "id": "79a6899a0013ebd6",
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "35    59\n29    55\n36    55\n34    53\n31    48\n30    48\n32    47\n40    47\n33    47\n38    39\n27    38\n37    37\n28    35\n42    34\n41    31\n26    31\n45    30\n39    30\n46    25\n43    25\n50    23\n44    22\n49    22\n25    20\n24    18\n55    17\n51    16\n47    16\n54    15\n56    13\n53    13\n48    13\n22    12\n52    12\n23    10\n19     8\n59     8\n58     7\n21     7\n20     6\n60     4\n18     2\n57     2\nName: Age, dtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:45:19.644778200Z",
     "start_time": "2024-01-29T11:45:19.634267300Z"
    }
   },
   "id": "4fd8f2196cdb34ee",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 还想进一步提升\n",
    "    - 尝试造新的特征 总工龄/在几家公司任职\n",
    "    - SMOTE过采样\n",
    "    - 数据清洗 孤立森林"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b037a0e8da14318d"
  }
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